pytorch/test/cpp/api
anjali411 762be86e63 [C++ API Parity] [Optimizers] added closure to optimizers (#34790)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/34790

Differential Revision: D20468361

Pulled By: anjali411

fbshipit-source-id: 1c6115d735b211dc2bedf002d58931cb32cf657a
2020-03-16 07:51:44 -07:00
..
any.cpp [C++ API] Allow skipping default arguments in module's forward method when module is used in Sequential (#33027) 2020-02-17 20:38:02 -08:00
autograd.cpp [autograd] fix allow_unused checking for C++ API (#34035) 2020-03-02 17:57:15 -08:00
CMakeLists.txt Remove using namespace torch::autograd from header files (#34423) 2020-03-09 10:31:21 -07:00
dataloader.cpp Fix typos (#30606) 2019-12-02 20:17:42 -08:00
dispatch.cpp Add the build for runtime dispatch for AVX, AVX2 instruction set (#26125) 2020-03-10 15:32:57 -07:00
enum.cpp [C++ API] RNN / GRU / LSTM layer refactoring (#34322) 2020-03-15 17:48:29 -07:00
expanding-array.cpp
functional.cpp Fix torch::allclose to handle std::numeric_limits<T>::lowest() for integral types (#32978) 2020-02-04 19:06:52 -08:00
init.cpp [C++ API] Remove deprecated torch::nn::BatchNorm / FeatureDropout / modules_ordered_dict and torch::nn::init::Nonlinearity / FanMode (#34508) 2020-03-12 10:09:58 -07:00
init_baseline.h
init_baseline.py
integration.cpp [C++ API] Remove deprecated torch::nn::BatchNorm / FeatureDropout / modules_ordered_dict and torch::nn::init::Nonlinearity / FanMode (#34508) 2020-03-12 10:09:58 -07:00
jit.cpp Remove attempToRecoverType (#26767) 2019-10-16 11:07:13 -07:00
memory.cpp
misc.cpp
module.cpp Remove dead includes in caffe2/test 2020-01-21 11:30:34 -08:00
modulelist.cpp [C++ API] RNN / GRU / LSTM layer refactoring (#34322) 2020-03-15 17:48:29 -07:00
modules.cpp [C++ API] RNNCell / LSTMCell / GRUCell layers (#34400) 2020-03-13 21:52:24 -07:00
namespace.cpp Remove using namespace torch::autograd from header files (#34423) 2020-03-09 10:31:21 -07:00
nn_utils.cpp [C++ API] Add PackedSequence / pack_padded_sequence / pad_packed_sequence / pack_sequence (#33652) 2020-02-25 12:53:41 -08:00
optim.cpp [C++ API Parity] [Optimizers] added closure to optimizers (#34790) 2020-03-16 07:51:44 -07:00
optim_baseline.h
optim_baseline.py
ordered_dict.cpp
parallel.cpp Fix bugs in torch::tensor constructor (#28523) 2019-10-31 12:53:06 -07:00
README.md
rnn.cpp [C++ API] RNN / GRU / LSTM layer refactoring (#34322) 2020-03-15 17:48:29 -07:00
sequential.cpp [C++ API] RNN / GRU / LSTM layer refactoring (#34322) 2020-03-15 17:48:29 -07:00
serialize.cpp [C++ API Parity] [Optimizers] added closure to optimizers (#34790) 2020-03-16 07:51:44 -07:00
static.cpp
support.cpp Use default dtype for torch::tensor(floating_point_values) and torch::tensor(empty braced-init-list) when dtype is not specified (#29632) 2019-11-13 15:17:11 -08:00
support.h C++ tensor indexing: more indexing tests (#30427) 2020-02-28 22:07:41 -08:00
tensor.cpp Bug fixes: torch::tensor(floating-point values) -> default dtype, and torch::tensor(integer values) ->at::kLong (#32367) 2020-02-01 15:00:07 -08:00
tensor_cuda.cpp Fix MagmaInitializesCorrectly_CUDA by using an invertible matrix (#32547) 2020-01-25 20:00:54 -08:00
tensor_indexing.cpp [C++ API] Remove init-list form of at::indexing::Slice (#34255) 2020-03-06 05:51:53 -08:00
tensor_options.cpp Deprecate tensor.type() (#30281) 2019-12-05 10:55:34 -08:00
tensor_options_cuda.cpp Deprecate tensor.type() (#30281) 2019-12-05 10:55:34 -08:00
torch_include.cpp Relax set_num_threads restriction in parallel native case (#27947) 2019-10-16 21:53:36 -07:00

C++ Frontend Tests

In this folder live the tests for PyTorch's C++ Frontend. They use the GoogleTest test framework.

CUDA Tests

To make a test runnable only on platforms with CUDA, you should suffix your test with _CUDA, e.g.

TEST(MyTestSuite, MyTestCase_CUDA) { }

To make it runnable only on platforms with at least two CUDA machines, suffix it with _MultiCUDA instead of _CUDA, e.g.

TEST(MyTestSuite, MyTestCase_MultiCUDA) { }

There is logic in main.cpp that detects the availability and number of CUDA devices and supplies the appropriate negative filters to GoogleTest.

Integration Tests

Integration tests use the MNIST dataset. You must download it by running the following command from the PyTorch root folder:

$ python tools/download_mnist.py -d test/cpp/api/mnist

The required paths will be referenced as test/cpp/api/mnist/... in the test code, so you must run the integration tests from the PyTorch root folder.